


import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)
def 							UpperCamelCase_(					_A :Tuple					,  _A :str	)->		List[str]:
			UpperCamelCase__       =							checkpoint

			UpperCamelCase__       =							{}

			UpperCamelCase__       =							vae_state_dict["encoder.conv_in.weight"]
			UpperCamelCase__       =							vae_state_dict["encoder.conv_in.bias"]
			UpperCamelCase__       =							vae_state_dict["encoder.conv_out.weight"]
			UpperCamelCase__       =							vae_state_dict["encoder.conv_out.bias"]
			UpperCamelCase__       =							vae_state_dict["encoder.norm_out.weight"]
			UpperCamelCase__       =							vae_state_dict["encoder.norm_out.bias"]

			UpperCamelCase__       =							vae_state_dict["decoder.conv_in.weight"]
			UpperCamelCase__       =							vae_state_dict["decoder.conv_in.bias"]
			UpperCamelCase__       =							vae_state_dict["decoder.conv_out.weight"]
			UpperCamelCase__       =							vae_state_dict["decoder.conv_out.bias"]
			UpperCamelCase__       =							vae_state_dict["decoder.norm_out.weight"]
			UpperCamelCase__       =							vae_state_dict["decoder.norm_out.bias"]

			UpperCamelCase__       =							vae_state_dict["quant_conv.weight"]
			UpperCamelCase__       =							vae_state_dict["quant_conv.bias"]
			UpperCamelCase__       =							vae_state_dict["post_quant_conv.weight"]
			UpperCamelCase__       =							vae_state_dict["post_quant_conv.bias"]

			# Retrieves the keys for the encoder down blocks only
			UpperCamelCase__       =							len({".".join(layer.split("."	)[:3]	) for layer in vae_state_dict if "encoder.down" in layer}	)
			UpperCamelCase__       =							{
			    layer_id: [key for key in vae_state_dict if F'''down.{layer_id}''' in key] for layer_id in range(_A	)
			}

			# Retrieves the keys for the decoder up blocks only
			UpperCamelCase__       =							len({".".join(layer.split("."	)[:3]	) for layer in vae_state_dict if "decoder.up" in layer}	)
			UpperCamelCase__       =							{
			    layer_id: [key for key in vae_state_dict if F'''up.{layer_id}''' in key] for layer_id in range(_A	)
			}

			for i in range(_A	):
						UpperCamelCase__       =							[key for key in down_blocks[i] if F'''down.{i}''' in key and F'''down.{i}.downsample''' not in key]

						if F'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
									UpperCamelCase__       =							vae_state_dict.pop(
									    F'''encoder.down.{i}.downsample.conv.weight'''	)
									UpperCamelCase__       =							vae_state_dict.pop(
									    F'''encoder.down.{i}.downsample.conv.bias'''	)

						UpperCamelCase__       =							renew_vae_resnet_paths(_A	)
						UpperCamelCase__       =							{"old": F'''down.{i}.block''', "new": F'''down_blocks.{i}.resnets'''}
						assign_to_checkpoint(_A					,  _A					,  _A					,  additional_replacements=[meta_path]					,  config=_A	)

			UpperCamelCase__       =							[key for key in vae_state_dict if "encoder.mid.block" in key]
			UpperCamelCase__       =							2
			for i in range(1					,  num_mid_res_blocks + 1	):
						UpperCamelCase__       =							[key for key in mid_resnets if F'''encoder.mid.block_{i}''' in key]

						UpperCamelCase__       =							renew_vae_resnet_paths(_A	)
						UpperCamelCase__       =							{"old": F'''mid.block_{i}''', "new": F'''mid_block.resnets.{i - 1}'''}
						assign_to_checkpoint(_A					,  _A					,  _A					,  additional_replacements=[meta_path]					,  config=_A	)

			UpperCamelCase__       =							[key for key in vae_state_dict if "encoder.mid.attn" in key]
			UpperCamelCase__       =							renew_vae_attention_paths(_A	)
			UpperCamelCase__       =							{"old": "mid.attn_1", "new": "mid_block.attentions.0"}
			assign_to_checkpoint(_A					,  _A					,  _A					,  additional_replacements=[meta_path]					,  config=_A	)
			conv_attn_to_linear(_A	)

			for i in range(_A	):
						UpperCamelCase__       =							num_up_blocks - 1 - i
						UpperCamelCase__       =							[
						    key for key in up_blocks[block_id] if F'''up.{block_id}''' in key and F'''up.{block_id}.upsample''' not in key
						]

						if F'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
									UpperCamelCase__       =							vae_state_dict[
									    F'''decoder.up.{block_id}.upsample.conv.weight'''
									]
									UpperCamelCase__       =							vae_state_dict[
									    F'''decoder.up.{block_id}.upsample.conv.bias'''
									]

						UpperCamelCase__       =							renew_vae_resnet_paths(_A	)
						UpperCamelCase__       =							{"old": F'''up.{block_id}.block''', "new": F'''up_blocks.{i}.resnets'''}
						assign_to_checkpoint(_A					,  _A					,  _A					,  additional_replacements=[meta_path]					,  config=_A	)

			UpperCamelCase__       =							[key for key in vae_state_dict if "decoder.mid.block" in key]
			UpperCamelCase__       =							2
			for i in range(1					,  num_mid_res_blocks + 1	):
						UpperCamelCase__       =							[key for key in mid_resnets if F'''decoder.mid.block_{i}''' in key]

						UpperCamelCase__       =							renew_vae_resnet_paths(_A	)
						UpperCamelCase__       =							{"old": F'''mid.block_{i}''', "new": F'''mid_block.resnets.{i - 1}'''}
						assign_to_checkpoint(_A					,  _A					,  _A					,  additional_replacements=[meta_path]					,  config=_A	)

			UpperCamelCase__       =							[key for key in vae_state_dict if "decoder.mid.attn" in key]
			UpperCamelCase__       =							renew_vae_attention_paths(_A	)
			UpperCamelCase__       =							{"old": "mid.attn_1", "new": "mid_block.attentions.0"}
			assign_to_checkpoint(_A					,  _A					,  _A					,  additional_replacements=[meta_path]					,  config=_A	)
			conv_attn_to_linear(_A	)
			return new_checkpoint
def 							UpperCamelCase_(					_A :str					,  _A :str					,  )->		Union[str, Any]:
			# Only support V1
			UpperCamelCase__       =							requests.get(
			    " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"	)
			UpperCamelCase__       =							io.BytesIO(r.content	)

			UpperCamelCase__       =							OmegaConf.load(_A	)
			UpperCamelCase__       =							5_12
			UpperCamelCase__       =							"cuda" if torch.cuda.is_available() else "cpu"
			if checkpoint_path.endswith("safetensors"	):
						from safetensors import safe_open

						UpperCamelCase__       =							{}
						with safe_open(_A					,  framework="pt"					,  device="cpu"	) as f:
									for key in f.keys():
												UpperCamelCase__       =							f.get_tensor(_A	)
			else:
						UpperCamelCase__       =							torch.load(_A					,  map_location=_A	)["state_dict"]

			# Convert the VAE model.
			UpperCamelCase__       =							create_vae_diffusers_config(_A					,  image_size=_A	)
			UpperCamelCase__       =							custom_convert_ldm_vae_checkpoint(_A					,  _A	)

			UpperCamelCase__       =							AutoencoderKL(**_A	)
			vae.load_state_dict(_A	)
			vae.save_pretrained(_A	)


if __name__ == "__main__":
						__UpperCamelCase     		=							argparse.ArgumentParser()

						parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
						parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

						__UpperCamelCase     		=							parser.parse_args()

						vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		'xmod'




			def __init__(				self   ,    snake_case=30522   ,    snake_case=768   ,    snake_case=12   ,    snake_case=12   ,    snake_case=3072   ,    snake_case="gelu"   ,    snake_case=0.1   ,    snake_case=0.1   ,    snake_case=512   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=1E-1_2   ,    snake_case=1   ,    snake_case=0   ,    snake_case=2   ,    snake_case="absolute"   ,    snake_case=True   ,    snake_case=None   ,    snake_case=False   ,    snake_case=2   ,    snake_case=False   ,    snake_case=True   ,    snake_case=True   ,    snake_case=("en_XX",)   ,    snake_case=None   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(pad_token_id=snake_case   ,    bos_token_id=snake_case   ,    eos_token_id=snake_case   ,    **snake_case  )

						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							hidden_size
						UpperCamelCase__       =							num_hidden_layers
						UpperCamelCase__       =							num_attention_heads
						UpperCamelCase__       =							hidden_act
						UpperCamelCase__       =							intermediate_size
						UpperCamelCase__       =							hidden_dropout_prob
						UpperCamelCase__       =							attention_probs_dropout_prob
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							type_vocab_size
						UpperCamelCase__       =							initializer_range
						UpperCamelCase__       =							layer_norm_eps
						UpperCamelCase__       =							position_embedding_type
						UpperCamelCase__       =							use_cache
						UpperCamelCase__       =							classifier_dropout
						UpperCamelCase__       =							pre_norm
						UpperCamelCase__       =							adapter_reduction_factor
						UpperCamelCase__       =							adapter_layer_norm
						UpperCamelCase__       =							adapter_reuse_layer_norm
						UpperCamelCase__       =							ln_before_adapter
						UpperCamelCase__       =							list(snake_case  )
						UpperCamelCase__       =							default_language







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.task == "multiple-choice":
									UpperCamelCase__       =							{0: "batch", 1: "choice", 2: "sequence"}
						else:
									UpperCamelCase__       =							{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]  )


